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This article systematically summarises the digital twin technology in the existing scientific field using the bibliometric method, puts forward the critical areas and research directions at different stages of the life cycle of the digital twin research topic. Using R-Studio 'Biblioshiny' to measure scientifically, we visually analyze 514 articles related to digital twin in the Web of Science's core set from 2014 to 2021 (including SCI-Expanded and SSCI database). Firstly, we analyzed the core journals, articles, authors, institutions, relevant countries to determine the vital influence of digital twin-related literature. Secondly, we use theme maps to draw the research topics of digital twin-related literature as icons on the conceptual structure and offer four cluster parts (Motor themes, highly developed and isolated themes, Emerging themes, and Basic themes). Finally, we concluded by summarising the limitations of our study and subsequently highlighted future research directions. The study shows that monitoring, maintenance, and computer vision are research topics with good development momentum. Disturbance observer, reinforcement learning, and friction are relatively small research topics with a good development foundation, and scheduling, process modelling, and deep reinforcement learning are emerging research directions. [ABSTRACT FROM AUTHOR]

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Architectural heritage is vulnerable to disasters. Digital technologies can fight destruction and can ensure integrity by monitoring, managing and protecting architectural heritage from disasters. In this paper, we clarify the relationship between disasters, digitalization and architectural heritage conservation for the sustainability of cultural heritage. This study used the PRISMA process, and bibliometric tools VOSviewer and Citespace to explore the potential of digital technologies in the protection of architectural heritage—especially during disaster cycles, from the perspectives of both universal and typicality; the results revealed that digital twins, deep learning, and preventive conservation are currently hot topics in digital preservation research (especially that research which relates to disaster cycles). On this basis, this paper summarizes the relevant technologies involved in architectural heritage preservation from the perspective of the disaster cycle and the digital phase, and proposes three future research directions: accurate prediction of multi-disasters, automatic early warning of structural damages, and intelligent monitoring of human–computer interaction. This paper constructs a new research frame for digital preservation of architectural heritage during disasters, providing theoretical reference and practical guidance for architectural heritage conversation. [ABSTRACT FROM AUTHOR]

Digital twin is the digital representation of an entity, and it drives Industry 4.0. This paper presents a bibliometric analysis of digital twin in the supply chain to help researchers, industry practitioners, and academics to understand the trend, development, and focus of the areas of digital twin in the supply chain. This paper found several key clusters of research, including the designing of a digital twin model, integration of a digital twin model, application of digital twin in quality control, and digital twin in digitalization. In the embryonic stage of research, digital twin was tested in the production line with limited optimization. In the development stage, the importance of digital twin in Industry 4.0 was observed, as big data, machine learning, Industrial Internet of Things, blockchain, edge computing, and cloud-based systems complemented digital twin models. Digital twin was applied to improve sustainability in manufacturing and production logistics. In the current prosperity stage with high annual publications, the recent trends of this topic focus on the integration of deep learning, data models, and artificial intelligence for digitalization. This bibliometric analysis also found that the COVID-19 pandemic drove the start of the prosperity stage of digital twin research in the supply chain. Researchers in this field are slowly moving towards applying digital twin for human-centric systems and mass personalization to prepare to transit to Industry 5.0. [ABSTRACT FROM AUTHOR]

Construction faces many safety accidents with urbanization, particularly in hoisting. However, there is a lack of systematic review studies in this area. This paper explored the factors and methods of risk assessment in hoisting for industrial building system (IBS) construction. Firstly, bibliometric analysis revealed that future research will focus on "ergonomics", "machine learning", "computer simulation", and "wearable sensors". Secondly, the previous 80 factors contributing to hoisting risks were summarized from a "human–equipment–management–material–environment" perspective, which can serve as a reference point for managers. Finally, we discussed, in-depth, the application of artificial neural networks (ANNs) and digital twins (DT). ANNs have improved the efficiency and accuracy of risk assessment. Still, they require high-quality and significant data, which traditional methods do not provide, resulting in the low accuracy of risk simulation results. DT data are emerging as an alternative, enabling stakeholders to visualize and analyze the construction process. However, DT's interactivity, high cost, and information security need further improvement. Based on the discussion and analysis, the risk control model created in this paper guides the direction for future research. [ABSTRACT FROM AUTHOR]

The industrial transformation led by digitization-related technologies has attracted research attention in recent decades, enhancing its application in different sectors. The transport industry is a crucial driving force for economic growth and social development. It is still necessary to make transportation infrastructure and services safer, cleaner, and more affordable to cope with increasing urbanization and mobility. This paper systematically examines the science mapping of building information modeling and digital twins technologies in the digitalization of transportation. Through the bibliometric and content analysis approaches, 493 related documents were screened and analyzed from the Web of Science and Scopus databases. The software programs VOSviewer and Bibliometrix were used to determine research trends and current gaps, which will be beneficial to future research in this vital field. The results showed that over 80% of the relevant documents have been published since 2018. China is the most productive country, followed by the United States and Italy, and Germany is the most cited and influential country. Moreover, research also revealed the leading authors, top journals, and highly cited papers. The findings may be used as a guide for: (1) improving the efficiency of intelligent transportation system element management; (2) the development and application of digital technologies; (3) the flow and goals of entire-life-cycle management; and (4) the optimization of related algorithms and models. [ABSTRACT FROM AUTHOR]

Purpose: Although organizations have more data than ever at their disposal, actually deriving meaningful insights and actions from them is easier said than done. In this concern, the main objective of this study is to identify trends and research opportunities regarding data management within new product development (NPD) and collaborative engineering. Design/methodology/approach: Bibliometric and systemic analyses have been carried out using the methodological procedure ProKnow-C, which provides a structured framework for the literature review. A bibliographic portfolio (BP) was consolidated with 33 papers that represent the state of art in the subject. Findings: Most recent researches within the BP indicate new trends and paradigm shifts in this area of research, tackling subjects such as the internet of things, cloud computing, big data analytics and digital twin. Research gaps include the lack of data automation and the absence of a common architecture for systems integration. However, from a general perspective of the BP, the management of experimental data is suggested as a research opportunity for future works. Although many studies have tackled data and collaboration based on computer-aided technologies environments, no study examined the management of the measured data collected during the verification and validation stages of a product. Originality/value: This work provides a fresh and relevant source of authors, journals and studies for researchers and practitioners interested in the domain of data management applied to NPD and collaborative engineering. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) have received considerable attention as an emerging technology in recent years, which can offer advantages for improving the performance of infrastructures. However, the inherent complexity of infrastructures alongside the nascent nature of digital twins in the Architecture, Engineering, and Construction (AEC) industry hinders the adoption of infrastructure digital twins (IDTs). The lack of common understanding among different stakeholders has been noted as one of the most significant roadblocks to implementing IDTs in practice. This study is a quantitative attempt to address this gap by providing different stakeholders with a multi-layer knowledge map by analyzing 139 identified IDTs in three levels of bibliometric and social network analyses. First, knowledge themes are extracted from the most-frequent journals to provide an overview of IDT knowledge. Second, a combination of co-citation analysis and social network theories illustrated six clusters of IDT knowledge and their relationships. Third, the co-occurrence network of keywords revealed where, why, and what enabling technologies have been employed so far. These findings were synthesized into a three-layer knowledge map to not only illustrate the maturity level and evolution potentials of each layer but also serve as a hierarchical strategic plan recommending future direction to decision-makers, researchers, and practitioners.

Construction faces many safety accidents with urbanization, particularly in hoisting. However, there is a lack of systematic review studies in this area. This paper explored the factors and methods of risk assessment in hoisting for industrial building system (IBS) construction. Firstly, bibliometric analysis revealed that future research will focus on “ergonomics”, “machine learning”, “computer simulation”, and “wearable sensors”. Secondly, the previous 80 factors contributing to hoisting risks were summarized from a “human–equipment–management–material–environment” perspective, which can serve as a reference point for managers. Finally, we discussed, in-depth, the application of artificial neural networks (ANNs) and digital twins (DT). ANNs have improved the efficiency and accuracy of risk assessment. Still, they require high-quality and significant data, which traditional methods do not provide, resulting in the low accuracy of risk simulation results. DT data are emerging as an alternative, enabling stakeholders to visualize and analyze the construction process. However, DT’s interactivity, high cost, and information security need further improvement. Based on the discussion and analysis, the risk control model created in this paper guides the direction for future research.

The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking. [ABSTRACT FROM AUTHOR]

The industrial transformation led by digitization-related technologies has attracted research attention in recent decades, enhancing its application in different sectors. The transport industry is a crucial driving force for economic growth and social development. It is still necessary to make transportation infrastructure and services safer, cleaner, and more affordable to cope with increasing urbanization and mobility. This paper systematically examines the science mapping of building information modeling and digital twins technologies in the digitalization of transportation. Through the bibliometric and content analysis approaches, 493 related documents were screened and analyzed from the Web of Science and Scopus databases. The software programs VOSviewer and Bibliometrix were used to determine research trends and current gaps, which will be beneficial to future research in this vital field. The results showed that over 80% of the relevant documents have been published since 2018. China is the most productive country, followed by the United States and Italy, and Germany is the most cited and influential country. Moreover, research also revealed the leading authors, top journals, and highly cited papers. The findings may be used as a guide for: (1) improving the efficiency of intelligent transportation system element management; (2) the development and application of digital technologies; (3) the flow and goals of entire-life-cycle management; and (4) the optimization of related algorithms and models.

With the advancement of Industry 4.0 and the development of science and technology, industries have seen the advantages of digital twin (DT) implementations and concepts. Although the construction industry is still in the early stage of DT development and implementation, it is urgent for the industry to have a DT agenda. This paper identifies 1158 related bibliographic records from the Web of Science and 745 records from the Derwent Patent Database and quantitatively analyzed them to describe patterns of publications. Two knowledge mapping tools were selected to visualize and analyze the literature in the relevant scientific domain. Then, we integrated clustering, knowledge mapping, and network analysis methods to show the current foci and future directions of DT-MT derived from the data of time distribution, journal areas, and subject distribution. The results indicate that DT is a cross-disciplinary information technology with great development potential, and its development in the construction industry is scattered with the leading advancement in the manufacturing of construction materials and components. The derived knowledge map shows three stages of construction DT, including (1) data collection and creation of DT framework and database; (2) implementation of planning and design modules in detail and control of construction sites; and (3) carrying out asset management and fault prediction in the operation and maintenance phase of projects. The novelty of this paper is the specific analysis of DT in the construction industry. This study adds value to the architecture, engineering, and construction industries by shedding light on the focus of DT practical applications and the formation of a development path of construction DT. • This paper seeks to improve the understanding of Digital Twins in the construction industry. • Helps relevant industries to develop a well-structured development path. • Smart engineering, manufacturing, and construction affect industrial performance and the quality of construction projects. • Integration of clustering, knowledge mapping, and network analysis methods to identify the current foci and future directions. • Prediction of the technology influences and its clean production to enhance domain-general strategy knowledge. [ABSTRACT FROM AUTHOR]

The interest of the academic and practitioner communities on the topic of Digital Twin has grown substantially in recent years. Bibliometric analysis can serve as a useful tool to explore the roadmap of the Digital Twin across various emergent themes over time. In this paper, we compare and analyze 1270 news articles and 4036 research publications to assess the evolution of the Digital Twin paradigm according to these sources from 2016 to 2021. We apply topic modeling and sentiment analysis on the textual corpora. Our analysis shows that certain topics related to applications, simulation, and enabling technologies for Digital Twin find greater coverage and generate higher positivity over time. We ascertain the coevolution and divergence in the number and sentiment of topics through curve matching metrics and determine whether they can rouse consumer interest, captured through online search trends. Our regression analysis shows that news on applications of Digital Twin and research on process evaluation through real-time simulation significantly impact the search frequency of consumers. Our research helps the digital product and service providers to understand the academia-industry gap in their effort to investigate Digital Twin and guides them on steps to take and themes to pursue for generating consumer interest. • We conduct bibliometric analysis of 1270 news articles and 4036 research publications on Digital Twin. • We use topic modeling to identify the evolution of topics for Digital Twin from 2016 to 2021. • We examine the change in sentiments generated by the topics on Digital Twin over time. • We determine the consumer interest on Digital Twin through search queries on Google Trends. • We use regression analysis to ascertain the impact of news and research on consumer search. [ABSTRACT FROM AUTHOR]